/*
 *    This program is free software; you can redistribute it and/or modify
 *    it under the terms of the GNU General Public License as published by
 *    the Free Software Foundation; either version 2 of the License, or
 *    (at your option) any later version.
 *
 *    This program is distributed in the hope that it will be useful,
 *    but WITHOUT ANY WARRANTY; without even the implied warranty of
 *    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *    GNU General Public License for more details.
 *
 *    You should have received a copy of the GNU General Public License
 *    along with this program; if not, write to the Free Software
 *    Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
 */

/*
 *    RandomSplitResultProducer.java
 *    Copyright (C) 1999 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.experiment;

import weka.core.AdditionalMeasureProducer;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.RevisionHandler;
import weka.core.RevisionUtils;
import weka.core.Utils;

import java.io.File;
import java.util.Calendar;
import java.util.Enumeration;
import java.util.Random;
import java.util.TimeZone;
import java.util.Vector;

/**
 * <!-- globalinfo-start --> Generates a single train/test split and calls the
 * appropriate SplitEvaluator to generate some results.
 * <p/>
 * <!-- globalinfo-end -->
 * 
 * <!-- options-start --> Valid options are:
 * <p/>
 * 
 * <pre>
 * -P &lt;percent&gt;
 *  The percentage of instances to use for training.
 *  (default 66)
 * </pre>
 * 
 * <pre>
 * -D
 * Save raw split evaluator output.
 * </pre>
 * 
 * <pre>
 * -O &lt;file/directory name/path&gt;
 *  The filename where raw output will be stored.
 *  If a directory name is specified then then individual
 *  outputs will be gzipped, otherwise all output will be
 *  zipped to the named file. Use in conjuction with -D. (default splitEvalutorOut.zip)
 * </pre>
 * 
 * <pre>
 * -W &lt;class name&gt;
 *  The full class name of a SplitEvaluator.
 *  eg: weka.experiment.ClassifierSplitEvaluator
 * </pre>
 * 
 * <pre>
 * -R
 *  Set when data is not to be randomized and the data sets' size.
 *  Is not to be determined via probabilistic rounding.
 * </pre>
 * 
 * <pre>
 * Options specific to split evaluator weka.experiment.ClassifierSplitEvaluator:
 * </pre>
 * 
 * <pre>
 * -W &lt;class name&gt;
 *  The full class name of the classifier.
 *  eg: weka.classifiers.bayes.NaiveBayes
 * </pre>
 * 
 * <pre>
 * -C &lt;index&gt;
 *  The index of the class for which IR statistics
 *  are to be output. (default 1)
 * </pre>
 * 
 * <pre>
 * -I &lt;index&gt;
 *  The index of an attribute to output in the
 *  results. This attribute should identify an
 *  instance in order to know which instances are
 *  in the test set of a cross validation. if 0
 *  no output (default 0).
 * </pre>
 * 
 * <pre>
 * -P
 *  Add target and prediction columns to the result
 *  for each fold.
 * </pre>
 * 
 * <pre>
 * Options specific to classifier weka.classifiers.rules.ZeroR:
 * </pre>
 * 
 * <pre>
 * -D
 *  If set, classifier is run in debug mode and
 *  may output additional info to the console
 * </pre>
 * 
 * <!-- options-end -->
 * 
 * All options after -- will be passed to the split evaluator.
 * 
 * @author Len Trigg (trigg@cs.waikato.ac.nz)
 * @version $Revision: 6255 $
 */
public class RandomSplitResultProducer implements ResultProducer,
		OptionHandler, AdditionalMeasureProducer, RevisionHandler {

	/** for serialization */
	static final long serialVersionUID = 1403798165056795073L;

	/** The dataset of interest */
	protected Instances m_Instances;

	/** The ResultListener to send results to */
	protected ResultListener m_ResultListener = new CSVResultListener();

	/** The percentage of instances to use for training */
	protected double m_TrainPercent = 66;

	/** Whether dataset is to be randomized */
	protected boolean m_randomize = true;

	/** The SplitEvaluator used to generate results */
	protected SplitEvaluator m_SplitEvaluator = new ClassifierSplitEvaluator();

	/** The names of any additional measures to look for in SplitEvaluators */
	protected String[] m_AdditionalMeasures = null;

	/** Save raw output of split evaluators --- for debugging purposes */
	protected boolean m_debugOutput = false;

	/** The output zipper to use for saving raw splitEvaluator output */
	protected OutputZipper m_ZipDest = null;

	/** The destination output file/directory for raw output */
	protected File m_OutputFile = new File(new File(
			System.getProperty("user.dir")), "splitEvalutorOut.zip");

	/** The name of the key field containing the dataset name */
	public static String DATASET_FIELD_NAME = "Dataset";

	/** The name of the key field containing the run number */
	public static String RUN_FIELD_NAME = "Run";

	/** The name of the result field containing the timestamp */
	public static String TIMESTAMP_FIELD_NAME = "Date_time";

	/**
	 * Returns a string describing this result producer
	 * 
	 * @return a description of the result producer suitable for displaying in
	 *         the explorer/experimenter gui
	 */
	public String globalInfo() {
		return "Generates a single train/test split and calls the appropriate "
				+ "SplitEvaluator to generate some results.";
	}

	/**
	 * Sets the dataset that results will be obtained for.
	 * 
	 * @param instances
	 *            a value of type 'Instances'.
	 */
	public void setInstances(Instances instances) {

		m_Instances = instances;
	}

	/**
	 * Set a list of method names for additional measures to look for in
	 * SplitEvaluators. This could contain many measures (of which only a subset
	 * may be produceable by the current SplitEvaluator) if an experiment is the
	 * type that iterates over a set of properties.
	 * 
	 * @param additionalMeasures
	 *            an array of measure names, null if none
	 */
	public void setAdditionalMeasures(String[] additionalMeasures) {
		m_AdditionalMeasures = additionalMeasures;

		if (m_SplitEvaluator != null) {
			System.err.println("RandomSplitResultProducer: setting additional "
					+ "measures for " + "split evaluator");
			m_SplitEvaluator.setAdditionalMeasures(m_AdditionalMeasures);
		}
	}

	/**
	 * Returns an enumeration of any additional measure names that might be in
	 * the SplitEvaluator
	 * 
	 * @return an enumeration of the measure names
	 */
	public Enumeration enumerateMeasures() {
		Vector newVector = new Vector();
		if (m_SplitEvaluator instanceof AdditionalMeasureProducer) {
			Enumeration en = ((AdditionalMeasureProducer) m_SplitEvaluator)
					.enumerateMeasures();
			while (en.hasMoreElements()) {
				String mname = (String) en.nextElement();
				newVector.addElement(mname);
			}
		}
		return newVector.elements();
	}

	/**
	 * Returns the value of the named measure
	 * 
	 * @param additionalMeasureName
	 *            the name of the measure to query for its value
	 * @return the value of the named measure
	 * @throws IllegalArgumentException
	 *             if the named measure is not supported
	 */
	public double getMeasure(String additionalMeasureName) {
		if (m_SplitEvaluator instanceof AdditionalMeasureProducer) {
			return ((AdditionalMeasureProducer) m_SplitEvaluator)
					.getMeasure(additionalMeasureName);
		} else {
			throw new IllegalArgumentException("RandomSplitResultProducer: "
					+ "Can't return value for : " + additionalMeasureName
					+ ". " + m_SplitEvaluator.getClass().getName() + " "
					+ "is not an AdditionalMeasureProducer");
		}
	}

	/**
	 * Sets the object to send results of each run to.
	 * 
	 * @param listener
	 *            a value of type 'ResultListener'
	 */
	public void setResultListener(ResultListener listener) {

		m_ResultListener = listener;
	}

	/**
	 * Gets a Double representing the current date and time. eg: 1:46pm on
	 * 20/5/1999 -> 19990520.1346
	 * 
	 * @return a value of type Double
	 */
	public static Double getTimestamp() {

		Calendar now = Calendar.getInstance(TimeZone.getTimeZone("UTC"));
		double timestamp = now.get(Calendar.YEAR) * 10000
				+ (now.get(Calendar.MONTH) + 1) * 100
				+ now.get(Calendar.DAY_OF_MONTH)
				+ now.get(Calendar.HOUR_OF_DAY) / 100.0
				+ now.get(Calendar.MINUTE) / 10000.0;
		return new Double(timestamp);
	}

	/**
	 * Prepare to generate results.
	 * 
	 * @throws Exception
	 *             if an error occurs during preprocessing.
	 */
	public void preProcess() throws Exception {

		if (m_SplitEvaluator == null) {
			throw new Exception("No SplitEvalutor set");
		}
		if (m_ResultListener == null) {
			throw new Exception("No ResultListener set");
		}
		m_ResultListener.preProcess(this);
	}

	/**
	 * Perform any postprocessing. When this method is called, it indicates that
	 * no more requests to generate results for the current experiment will be
	 * sent.
	 * 
	 * @throws Exception
	 *             if an error occurs
	 */
	public void postProcess() throws Exception {

		m_ResultListener.postProcess(this);
		if (m_debugOutput) {
			if (m_ZipDest != null) {
				m_ZipDest.finished();
				m_ZipDest = null;
			}
		}
	}

	/**
	 * Gets the keys for a specified run number. Different run numbers
	 * correspond to different randomizations of the data. Keys produced should
	 * be sent to the current ResultListener
	 * 
	 * @param run
	 *            the run number to get keys for.
	 * @throws Exception
	 *             if a problem occurs while getting the keys
	 */
	public void doRunKeys(int run) throws Exception {
		if (m_Instances == null) {
			throw new Exception("No Instances set");
		}
		// Add in some fields to the key like run number, dataset name
		Object[] seKey = m_SplitEvaluator.getKey();
		Object[] key = new Object[seKey.length + 2];
		key[0] = Utils.backQuoteChars(m_Instances.relationName());
		key[1] = "" + run;
		System.arraycopy(seKey, 0, key, 2, seKey.length);
		if (m_ResultListener.isResultRequired(this, key)) {
			try {
				m_ResultListener.acceptResult(this, key, null);
			} catch (Exception ex) {
				// Save the train and test datasets for debugging purposes?
				throw ex;
			}
		}
	}

	/**
	 * Gets the results for a specified run number. Different run numbers
	 * correspond to different randomizations of the data. Results produced
	 * should be sent to the current ResultListener
	 * 
	 * @param run
	 *            the run number to get results for.
	 * @throws Exception
	 *             if a problem occurs while getting the results
	 */
	public void doRun(int run) throws Exception {

		if (getRawOutput()) {
			if (m_ZipDest == null) {
				m_ZipDest = new OutputZipper(m_OutputFile);
			}
		}

		if (m_Instances == null) {
			throw new Exception("No Instances set");
		}
		// Add in some fields to the key like run number, dataset name
		Object[] seKey = m_SplitEvaluator.getKey();
		Object[] key = new Object[seKey.length + 2];
		key[0] = Utils.backQuoteChars(m_Instances.relationName());
		key[1] = "" + run;
		System.arraycopy(seKey, 0, key, 2, seKey.length);
		if (m_ResultListener.isResultRequired(this, key)) {

			// Randomize on a copy of the original dataset
			Instances runInstances = new Instances(m_Instances);

			Instances train;
			Instances test;

			if (!m_randomize) {

				// Don't do any randomization
				int trainSize = Utils.round(runInstances.numInstances()
						* m_TrainPercent / 100);
				int testSize = runInstances.numInstances() - trainSize;
				train = new Instances(runInstances, 0, trainSize);
				test = new Instances(runInstances, trainSize, testSize);
			} else {
				Random rand = new Random(run);
				runInstances.randomize(rand);

				// Nominal class
				if (runInstances.classAttribute().isNominal()) {

					// create the subset for each classs
					int numClasses = runInstances.numClasses();
					Instances[] subsets = new Instances[numClasses + 1];
					for (int i = 0; i < numClasses + 1; i++) {
						subsets[i] = new Instances(runInstances, 10);
					}

					// divide instances into subsets
					Enumeration e = runInstances.enumerateInstances();
					while (e.hasMoreElements()) {
						Instance inst = (Instance) e.nextElement();
						if (inst.classIsMissing()) {
							subsets[numClasses].add(inst);
						} else {
							subsets[(int) inst.classValue()].add(inst);
						}
					}

					// Compactify them
					for (int i = 0; i < numClasses + 1; i++) {
						subsets[i].compactify();
					}

					// merge into train and test sets
					train = new Instances(runInstances,
							runInstances.numInstances());
					test = new Instances(runInstances,
							runInstances.numInstances());
					for (int i = 0; i < numClasses + 1; i++) {
						int trainSize = Utils.probRound(
								subsets[i].numInstances() * m_TrainPercent
										/ 100, rand);
						for (int j = 0; j < trainSize; j++) {
							train.add(subsets[i].instance(j));
						}
						for (int j = trainSize; j < subsets[i].numInstances(); j++) {
							test.add(subsets[i].instance(j));
						}
						// free memory
						subsets[i] = null;
					}
					train.compactify();
					test.compactify();

					// randomize the final sets
					train.randomize(rand);
					test.randomize(rand);
				} else {

					// Numeric target
					int trainSize = Utils.probRound(runInstances.numInstances()
							* m_TrainPercent / 100, rand);
					int testSize = runInstances.numInstances() - trainSize;
					train = new Instances(runInstances, 0, trainSize);
					test = new Instances(runInstances, trainSize, testSize);
				}
			}
			try {
				Object[] seResults = m_SplitEvaluator.getResult(train, test);
				Object[] results = new Object[seResults.length + 1];
				results[0] = getTimestamp();
				System.arraycopy(seResults, 0, results, 1, seResults.length);
				if (m_debugOutput) {
					String resultName = ("" + run + "."
							+ Utils.backQuoteChars(runInstances.relationName())
							+ "." + m_SplitEvaluator.toString()).replace(' ',
							'_');
					resultName = Utils.removeSubstring(resultName,
							"weka.classifiers.");
					resultName = Utils.removeSubstring(resultName,
							"weka.filters.");
					resultName = Utils.removeSubstring(resultName,
							"weka.attributeSelection.");
					m_ZipDest.zipit(m_SplitEvaluator.getRawResultOutput(),
							resultName);
				}
				m_ResultListener.acceptResult(this, key, results);
			} catch (Exception ex) {
				// Save the train and test datasets for debugging purposes?
				throw ex;
			}
		}
	}

	/**
	 * Gets the names of each of the columns produced for a single run. This
	 * method should really be static.
	 * 
	 * @return an array containing the name of each column
	 */
	public String[] getKeyNames() {

		String[] keyNames = m_SplitEvaluator.getKeyNames();
		// Add in the names of our extra key fields
		String[] newKeyNames = new String[keyNames.length + 2];
		newKeyNames[0] = DATASET_FIELD_NAME;
		newKeyNames[1] = RUN_FIELD_NAME;
		System.arraycopy(keyNames, 0, newKeyNames, 2, keyNames.length);
		return newKeyNames;
	}

	/**
	 * Gets the data types of each of the columns produced for a single run.
	 * This method should really be static.
	 * 
	 * @return an array containing objects of the type of each column. The
	 *         objects should be Strings, or Doubles.
	 */
	public Object[] getKeyTypes() {

		Object[] keyTypes = m_SplitEvaluator.getKeyTypes();
		// Add in the types of our extra fields
		Object[] newKeyTypes = new String[keyTypes.length + 2];
		newKeyTypes[0] = new String();
		newKeyTypes[1] = new String();
		System.arraycopy(keyTypes, 0, newKeyTypes, 2, keyTypes.length);
		return newKeyTypes;
	}

	/**
	 * Gets the names of each of the columns produced for a single run. This
	 * method should really be static.
	 * 
	 * @return an array containing the name of each column
	 */
	public String[] getResultNames() {

		String[] resultNames = m_SplitEvaluator.getResultNames();
		// Add in the names of our extra Result fields
		String[] newResultNames = new String[resultNames.length + 1];
		newResultNames[0] = TIMESTAMP_FIELD_NAME;
		System.arraycopy(resultNames, 0, newResultNames, 1, resultNames.length);
		return newResultNames;
	}

	/**
	 * Gets the data types of each of the columns produced for a single run.
	 * This method should really be static.
	 * 
	 * @return an array containing objects of the type of each column. The
	 *         objects should be Strings, or Doubles.
	 */
	public Object[] getResultTypes() {

		Object[] resultTypes = m_SplitEvaluator.getResultTypes();
		// Add in the types of our extra Result fields
		Object[] newResultTypes = new Object[resultTypes.length + 1];
		newResultTypes[0] = new Double(0);
		System.arraycopy(resultTypes, 0, newResultTypes, 1, resultTypes.length);
		return newResultTypes;
	}

	/**
	 * Gets a description of the internal settings of the result producer,
	 * sufficient for distinguishing a ResultProducer instance from another with
	 * different settings (ignoring those settings set through this interface).
	 * For example, a cross-validation ResultProducer may have a setting for the
	 * number of folds. For a given state, the results produced should be
	 * compatible. Typically if a ResultProducer is an OptionHandler, this
	 * string will represent the command line arguments required to set the
	 * ResultProducer to that state.
	 * 
	 * @return the description of the ResultProducer state, or null if no state
	 *         is defined
	 */
	public String getCompatibilityState() {

		String result = "-P " + m_TrainPercent;
		if (!getRandomizeData()) {
			result += " -R";
		}
		if (m_SplitEvaluator == null) {
			result += " <null SplitEvaluator>";
		} else {
			result += " -W " + m_SplitEvaluator.getClass().getName();
		}
		return result + " --";
	}

	/**
	 * Returns the tip text for this property
	 * 
	 * @return tip text for this property suitable for displaying in the
	 *         explorer/experimenter gui
	 */
	public String outputFileTipText() {
		return "Set the destination for saving raw output. If the rawOutput "
				+ "option is selected, then output from the splitEvaluator for "
				+ "individual train-test splits is saved. If the destination is a "
				+ "directory, "
				+ "then each output is saved to an individual gzip file; if the "
				+ "destination is a file, then each output is saved as an entry "
				+ "in a zip file.";
	}

	/**
	 * Get the value of OutputFile.
	 * 
	 * @return Value of OutputFile.
	 */
	public File getOutputFile() {

		return m_OutputFile;
	}

	/**
	 * Set the value of OutputFile.
	 * 
	 * @param newOutputFile
	 *            Value to assign to OutputFile.
	 */
	public void setOutputFile(File newOutputFile) {

		m_OutputFile = newOutputFile;
	}

	/**
	 * Returns the tip text for this property
	 * 
	 * @return tip text for this property suitable for displaying in the
	 *         explorer/experimenter gui
	 */
	public String randomizeDataTipText() {
		return "Do not randomize dataset and do not perform probabilistic rounding "
				+ "if false";
	}

	/**
	 * Get if dataset is to be randomized
	 * 
	 * @return true if dataset is to be randomized
	 */
	public boolean getRandomizeData() {
		return m_randomize;
	}

	/**
	 * Set to true if dataset is to be randomized
	 * 
	 * @param d
	 *            true if dataset is to be randomized
	 */
	public void setRandomizeData(boolean d) {
		m_randomize = d;
	}

	/**
	 * Returns the tip text for this property
	 * 
	 * @return tip text for this property suitable for displaying in the
	 *         explorer/experimenter gui
	 */
	public String rawOutputTipText() {
		return "Save raw output (useful for debugging). If set, then output is "
				+ "sent to the destination specified by outputFile";
	}

	/**
	 * Get if raw split evaluator output is to be saved
	 * 
	 * @return true if raw split evalutor output is to be saved
	 */
	public boolean getRawOutput() {
		return m_debugOutput;
	}

	/**
	 * Set to true if raw split evaluator output is to be saved
	 * 
	 * @param d
	 *            true if output is to be saved
	 */
	public void setRawOutput(boolean d) {
		m_debugOutput = d;
	}

	/**
	 * Returns the tip text for this property
	 * 
	 * @return tip text for this property suitable for displaying in the
	 *         explorer/experimenter gui
	 */
	public String trainPercentTipText() {
		return "Set the percentage of data to use for training.";
	}

	/**
	 * Get the value of TrainPercent.
	 * 
	 * @return Value of TrainPercent.
	 */
	public double getTrainPercent() {

		return m_TrainPercent;
	}

	/**
	 * Set the value of TrainPercent.
	 * 
	 * @param newTrainPercent
	 *            Value to assign to TrainPercent.
	 */
	public void setTrainPercent(double newTrainPercent) {

		m_TrainPercent = newTrainPercent;
	}

	/**
	 * Returns the tip text for this property
	 * 
	 * @return tip text for this property suitable for displaying in the
	 *         explorer/experimenter gui
	 */
	public String splitEvaluatorTipText() {
		return "The evaluator to apply to the test data. "
				+ "This may be a classifier, regression scheme etc.";
	}

	/**
	 * Get the SplitEvaluator.
	 * 
	 * @return the SplitEvaluator.
	 */
	public SplitEvaluator getSplitEvaluator() {

		return m_SplitEvaluator;
	}

	/**
	 * Set the SplitEvaluator.
	 * 
	 * @param newSplitEvaluator
	 *            new SplitEvaluator to use.
	 */
	public void setSplitEvaluator(SplitEvaluator newSplitEvaluator) {

		m_SplitEvaluator = newSplitEvaluator;
		m_SplitEvaluator.setAdditionalMeasures(m_AdditionalMeasures);
	}

	/**
	 * Returns an enumeration describing the available options..
	 * 
	 * @return an enumeration of all the available options.
	 */
	public Enumeration listOptions() {

		Vector newVector = new Vector(5);

		newVector.addElement(new Option(
				"\tThe percentage of instances to use for training.\n"
						+ "\t(default 66)", "P", 1, "-P <percent>"));

		newVector.addElement(new Option("Save raw split evaluator output.",
				"D", 0, "-D"));

		newVector
				.addElement(new Option(
						"\tThe filename where raw output will be stored.\n"
								+ "\tIf a directory name is specified then then individual\n"
								+ "\toutputs will be gzipped, otherwise all output will be\n"
								+ "\tzipped to the named file. Use in conjuction with -D."
								+ "\t(default splitEvalutorOut.zip)", "O", 1,
						"-O <file/directory name/path>"));

		newVector.addElement(new Option(
				"\tThe full class name of a SplitEvaluator.\n"
						+ "\teg: weka.experiment.ClassifierSplitEvaluator",
				"W", 1, "-W <class name>"));

		newVector
				.addElement(new Option(
						"\tSet when data is not to be randomized and the data sets' size.\n"
								+ "\tIs not to be determined via probabilistic rounding.",
						"R", 0, "-R"));

		if ((m_SplitEvaluator != null)
				&& (m_SplitEvaluator instanceof OptionHandler)) {
			newVector.addElement(new Option("", "", 0,
					"\nOptions specific to split evaluator "
							+ m_SplitEvaluator.getClass().getName() + ":"));
			Enumeration enu = ((OptionHandler) m_SplitEvaluator).listOptions();
			while (enu.hasMoreElements()) {
				newVector.addElement(enu.nextElement());
			}
		}
		return newVector.elements();
	}

	/**
	 * Parses a given list of options.
	 * <p/>
	 * 
	 * <!-- options-start --> Valid options are:
	 * <p/>
	 * 
	 * <pre>
	 * -P &lt;percent&gt;
	 *  The percentage of instances to use for training.
	 *  (default 66)
	 * </pre>
	 * 
	 * <pre>
	 * -D
	 * Save raw split evaluator output.
	 * </pre>
	 * 
	 * <pre>
	 * -O &lt;file/directory name/path&gt;
	 *  The filename where raw output will be stored.
	 *  If a directory name is specified then then individual
	 *  outputs will be gzipped, otherwise all output will be
	 *  zipped to the named file. Use in conjuction with -D. (default splitEvalutorOut.zip)
	 * </pre>
	 * 
	 * <pre>
	 * -W &lt;class name&gt;
	 *  The full class name of a SplitEvaluator.
	 *  eg: weka.experiment.ClassifierSplitEvaluator
	 * </pre>
	 * 
	 * <pre>
	 * -R
	 *  Set when data is not to be randomized and the data sets' size.
	 *  Is not to be determined via probabilistic rounding.
	 * </pre>
	 * 
	 * <pre>
	 * Options specific to split evaluator weka.experiment.ClassifierSplitEvaluator:
	 * </pre>
	 * 
	 * <pre>
	 * -W &lt;class name&gt;
	 *  The full class name of the classifier.
	 *  eg: weka.classifiers.bayes.NaiveBayes
	 * </pre>
	 * 
	 * <pre>
	 * -C &lt;index&gt;
	 *  The index of the class for which IR statistics
	 *  are to be output. (default 1)
	 * </pre>
	 * 
	 * <pre>
	 * -I &lt;index&gt;
	 *  The index of an attribute to output in the
	 *  results. This attribute should identify an
	 *  instance in order to know which instances are
	 *  in the test set of a cross validation. if 0
	 *  no output (default 0).
	 * </pre>
	 * 
	 * <pre>
	 * -P
	 *  Add target and prediction columns to the result
	 *  for each fold.
	 * </pre>
	 * 
	 * <pre>
	 * Options specific to classifier weka.classifiers.rules.ZeroR:
	 * </pre>
	 * 
	 * <pre>
	 * -D
	 *  If set, classifier is run in debug mode and
	 *  may output additional info to the console
	 * </pre>
	 * 
	 * <!-- options-end -->
	 * 
	 * All options after -- will be passed to the split evaluator.
	 * 
	 * @param options
	 *            the list of options as an array of strings
	 * @throws Exception
	 *             if an option is not supported
	 */
	public void setOptions(String[] options) throws Exception {

		setRawOutput(Utils.getFlag('D', options));
		setRandomizeData(!Utils.getFlag('R', options));

		String fName = Utils.getOption('O', options);
		if (fName.length() != 0) {
			setOutputFile(new File(fName));
		}

		String trainPct = Utils.getOption('P', options);
		if (trainPct.length() != 0) {
			setTrainPercent((new Double(trainPct)).doubleValue());
		} else {
			setTrainPercent(66);
		}

		String seName = Utils.getOption('W', options);
		if (seName.length() == 0) {
			throw new Exception("A SplitEvaluator must be specified with"
					+ " the -W option.");
		}
		// Do it first without options, so if an exception is thrown during
		// the option setting, listOptions will contain options for the actual
		// SE.
		setSplitEvaluator((SplitEvaluator) Utils.forName(SplitEvaluator.class,
				seName, null));
		if (getSplitEvaluator() instanceof OptionHandler) {
			((OptionHandler) getSplitEvaluator()).setOptions(Utils
					.partitionOptions(options));
		}
	}

	/**
	 * Gets the current settings of the result producer.
	 * 
	 * @return an array of strings suitable for passing to setOptions
	 */
	public String[] getOptions() {

		String[] seOptions = new String[0];
		if ((m_SplitEvaluator != null)
				&& (m_SplitEvaluator instanceof OptionHandler)) {
			seOptions = ((OptionHandler) m_SplitEvaluator).getOptions();
		}

		String[] options = new String[seOptions.length + 9];
		int current = 0;

		options[current++] = "-P";
		options[current++] = "" + getTrainPercent();

		if (getRawOutput()) {
			options[current++] = "-D";
		}

		if (!getRandomizeData()) {
			options[current++] = "-R";
		}

		options[current++] = "-O";
		options[current++] = getOutputFile().getName();

		if (getSplitEvaluator() != null) {
			options[current++] = "-W";
			options[current++] = getSplitEvaluator().getClass().getName();
		}
		options[current++] = "--";

		System.arraycopy(seOptions, 0, options, current, seOptions.length);
		current += seOptions.length;
		while (current < options.length) {
			options[current++] = "";
		}
		return options;
	}

	/**
	 * Gets a text descrption of the result producer.
	 * 
	 * @return a text description of the result producer.
	 */
	public String toString() {

		String result = "RandomSplitResultProducer: ";
		result += getCompatibilityState();
		if (m_Instances == null) {
			result += ": <null Instances>";
		} else {
			result += ": " + Utils.backQuoteChars(m_Instances.relationName());
		}
		return result;
	}

	/**
	 * Returns the revision string.
	 * 
	 * @return the revision
	 */
	public String getRevision() {
		return RevisionUtils.extract("$Revision: 6255 $");
	}
} // RandomSplitResultProducer
